What Is Data Socialization, and Why Should You Care?

Christopher Tozzi

Datasocialization is one of the newest buzzwords in the world of data analytics and management. What does data socialization mean, and what can it do for you? Find out in this post.

What Is Data Socialization?

In a nutshell, data socialization refers to the sharing of data and data analytics tools with all members of an organization. The key idea behind data socialization is to make data-driven insights available to everyone in a self-service fashion.

Another way of defining data socialization is to say that it involves the “democratization” of data. Whereas the typical business has traditionally assigned data analytics tasks to only a handful of employees who specialize in data management, the data socialization concept aims to involve everyone in the organization in collecting, managing, analyzing and reacting to data.

Why Does Data Socialization Matter?

Data socialization is innovative because it helps businesses to double down on their ability to leverage data.

Yet traditionally, the extent to which businesses have leveraged that data has been limited. As noted above, the ability to access and analyze business-critical data has typically been available only to a small team of data specialists. Unless data analytics or data management is an explicit part of your job title, you probably didn’t do much with data; instead, you relied on other people — the ones who specialized in data management — to collect and analyze your business’s data for you, then provide recommendations to you based on it.

From a business standpoint, this approach is not ideal, for two main reasons:

When a business relies on only a small group of data specialists to process all of its data, those specialists are likely to become overwhelmed. It’s difficult for a small group to process an entire business’s data single-handedly and deliver relevant insights and recommendations to every business unit. This is especially true today, when the amount of data that organizations collect is larger than ever.

In most cases, data specialists have a limited understanding of other parts of the business. Their ability to leverage data in ways that benefit other business units is therefore limited, too.

Data socialization aims to solve these challenges by placing data and data analytics tools directly in the hands of the people who can use them as part of their jobs.

For example, if you work in marketing, data socialization means that you can collect and analyze data related to marketing campaigns yourself, rather than depending on data specialists to perform that task for you. Because you know your business’s marketing needs better than anyone who does not specialize in marketing, you are better positioned than the rest of your organization to derive relevant insights from that data.

Similarly, a customer service specialist can benefit from data socialization by being able to access and analyze information related to each of the customers he or she supports.

Data socialization does not mean, by the way, that data specialists have no role to play in data socialization. They remain the experts, and they oversee the tools and processes that enable other parts of the organization to perform data self-service. But they are no longer solely responsible for data management.

Best Practices for Data Socialization

When you want to empower everyone in your organization with the ability to manage and interpret data, you need to approach data management somewhat differently than you would when only data specialists are involved in the process.

Most obviously, you need data management tools that enable self-service without requiring a great deal of expertise. This might seem difficult to achieve, but in fact, data integration and analytics are simpler today than they once were. Even your non-technical employees will likely be able to work with data much more effectively using modern data management tools than you might expect.

That said, the ability to deliver a streamlined data experience is important for enabling data socialization. By streamlined, I mean providing a data analytics process that is free of complex technical kinks. For example, you should not expect your non-data-specialist employees to be able to perform complex data transformation or data integration tasks. Nor should they be expected to clean up low quality data sets.

Instead, you need to provide them with data that is readily usable. Providing tools that enable them to visualize data easily is also important.

Conclusion

In today’s data-driven world, everyone in the business stands to benefit from being able to access and interpret data that is relevant to his or her role within the organization. By embracing data socialization, businesses can make data analytics more efficient and faster, reduce the burden they place on their data specialists and provide more relevant data-driven insights to employees who stand to gain the most from them.